Contributors to Teen Birth
Step 0: Load necessary libraries and datasets
Load libraries that we use
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## Attaching package: 'maps'## The following object is masked from 'package:purrr':
##
## mapStep 1: Load datasets
We consider three datasets: Teen birth data by county Education by county *Unemployment rate by county
## Parsed with column specification:
## cols(
## Year = col_double(),
## State = col_character(),
## County = col_character(),
## `State FIPS Code` = col_double(),
## `County FIPS Code` = col_double(),
## `Combined FIPS Code` = col_double(),
## `Birth Rate` = col_double(),
## `Lower Confidence Limit` = col_double(),
## `Upper Confidence Limit` = col_double()
## )## Warning: Missing column names filled in: 'X48' [48]## Parsed with column specification:
## cols(
## .default = col_double(),
## `FIPS Code` = col_character(),
## State = col_character(),
## `Area name` = col_character(),
## `Less than a high school diploma, 1970` = col_number(),
## `High school diploma only, 1970` = col_number(),
## `Some college (1-3 years), 1970` = col_number(),
## `Four years of college or higher, 1970` = col_number(),
## `Less than a high school diploma, 1980` = col_number(),
## `High school diploma only, 1980` = col_number(),
## `Some college (1-3 years), 1980` = col_number(),
## `Four years of college or higher, 1980` = col_number(),
## `Less than a high school diploma, 1990` = col_number(),
## `High school diploma only, 1990` = col_number(),
## `Some college or associate's degree, 1990` = col_number(),
## `Bachelor's degree or higher, 1990` = col_number(),
## `Less than a high school diploma, 2000` = col_number(),
## `High school diploma only, 2000` = col_number(),
## `Some college or associate's degree, 2000` = col_number(),
## `Bachelor's degree or higher, 2000` = col_number(),
## `Less than a high school diploma, 2013-17` = col_number()
## # ... with 4 more columns
## )## See spec(...) for full column specifications.## Parsed with column specification:
## cols(
## State = col_character(),
## `State Code` = col_character(),
## Region = col_character(),
## Division = col_character()
## )Step 2: Clean datasets for ease of use
Looking at the years that the three datasets have in common, we chose to use 2015 data for teen_birth and Unemployment datases. For Education, we use the 2013-2017 average.
Step 3: Join datasets into a master dataset
We
Step 4: Plotting birth rates against various education rates and unemployment rates
## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing non-finite values (stat_smooth).
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing non-finite values (stat_smooth).
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing non-finite values (stat_smooth).
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing non-finite values (stat_smooth).
## Warning: Removed 2 rows containing missing values (geom_point).
Linear model of birth rate as a function of unemployment rate and education
The education data divides the population into four groups: less than high school, high school, college, beyond college. Since the four categories add up to 100%, it is sufficient to only include three of the four rates in the linear model.
##
## Call:
## lm(formula = `Birth Rate` ~ UnemploymentRate + Less_than_high_school +
## high_school + college, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -50.624 -5.317 -0.643 4.622 49.176
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.31067 2.23906 12.644 < 2e-16 ***
## UnemploymentRate 0.68262 0.08822 7.738 1.35e-14 ***
## Less_than_high_school 1.01869 0.03475 29.316 < 2e-16 ***
## high_school -0.23215 0.03749 -6.193 6.69e-10 ***
## college -0.48924 0.03492 -14.011 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.514 on 3128 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.5801, Adjusted R-squared: 0.5796
## F-statistic: 1080 on 4 and 3128 DF, p-value: < 2.2e-16
Linear model of log(birth rate) as a function of unemployment rate and education
Adding in quadratic terms to the model
Adding in squareroot terms to the model
Using log of the independent variables
Using education and employment to predict square root of birth rate
##
## Call:
## lm(formula = `Birth Rate` ~ UnemploymentRate + Less_than_high_school +
## high_school + college + Region, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -42.623 -5.341 -0.588 4.455 53.584
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 27.59481 2.38614 11.565 < 2e-16 ***
## UnemploymentRate 0.61385 0.08798 6.977 3.67e-12 ***
## Less_than_high_school 0.80832 0.03904 20.706 < 2e-16 ***
## high_school -0.18392 0.04005 -4.593 4.55e-06 ***
## college -0.48397 0.03724 -12.995 < 2e-16 ***
## RegionNortheast -3.49239 0.65989 -5.292 1.29e-07 ***
## RegionSouth 4.99573 0.40216 12.422 < 2e-16 ***
## RegionWest 1.45183 0.50883 2.853 0.00436 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.187 on 3125 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.612, Adjusted R-squared: 0.6112
## F-statistic: 704.3 on 7 and 3125 DF, p-value: < 2.2e-16
